[Trainer] Add a progress bar for batches skipped (#11324)

src/transformers/trainer.py

@@ -29,6 +29,8 @@ from logging import StreamHandler
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
 
+from tqdm.auto import tqdm
+
 
 # Integrations must be imported before ML frameworks:
 from .integrations import (  # isort: split
@@ -1097,6 +1099,7 @@ class Trainer:
         start_time = time.time()
         epochs_trained = 0
         steps_trained_in_current_epoch = 0
+        steps_trained_progress_bar = None
 
         # Check if continuing training from a checkpoint
         if resume_from_checkpoint is not None and os.path.isfile(
@@ -1116,8 +1119,12 @@ class Trainer:
             if not args.ignore_data_skip:
                 logger.info(
                     f"  Will skip the first {epochs_trained} epochs then the first {steps_trained_in_current_epoch} "
-                    "batches in the first epoch."
+                    "batches in the first epoch. If this takes a lot of time, you can add the `--ignore_data_skip` "
+                    "flag to your launch command, but you will resume the training on data already seen by your model."
                 )
+                if self.is_local_process_zero() and not args.disable_tqdm:
+                    steps_trained_progress_bar = tqdm(total=steps_trained_in_current_epoch)
+                    steps_trained_progress_bar.set_description("Skipping the first batches")
 
         # Update the references
         self.callback_handler.model = self.model
@@ -1176,7 +1183,12 @@ class Trainer:
                 # Skip past any already trained steps if resuming training
                 if steps_trained_in_current_epoch > 0:
                     steps_trained_in_current_epoch -= 1
+                    if steps_trained_progress_bar is not None:
+                        steps_trained_progress_bar.update(1)
                     continue
+                elif steps_trained_progress_bar is not None:
+                    steps_trained_progress_bar.close()
+                    steps_trained_progress_bar = None
 
                 if step % args.gradient_accumulation_steps == 0:
                     self.control = self.callback_handler.on_step_begin(args, self.state, self.control)